Looks ugly:
df_cut = df_new[
(
(df_new['l_ext']==31) |
(df_new['l_ext']==22) |
(df_new['l_ext']==30) |
(df_new['l_ext']==25) |
(df_new['l_ext']==64)
)
]
Does not work:
df_cut = df_new[(df_new['l_ext'] in [31, 22, 30, 25, 64])]
Is there an elegant and working solution of the above “problem”?
Answers:
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Method 1
Use isin
df_new[df_new['l_ext'].isin([31, 22, 30, 25, 64])]
Method 2
You can use pd.DataFrame.query:
select_values = [31, 22, 30, 25, 64]
df_cut = df_new.query('l_ext in @select_values')
In the background, this uses the top-level pd.eval function.
All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0